An automatic esophageal cancer pathological image discriminating device based on a convolution neural network and a discriminating method thereof

Abstract

The invention discloses an esophageal cancer pathological image automatic discrimination device based on a convolution neural network and a discrimination method thereof. The device comprises an imageacquisition module, an image processing module, a data storage module, a migration learning module, a network training module and a discrimination module. The screening method of the invention comprises the following steps: 1, an image acquisition module collects pathological images and constructs an image database of pathological slices of esophageal cancer; 2, each pathological image database is expanded through an image processing module; 3, the expanded pre-training network pathological image data set is used to complete the migration learning; 4. on the basis of the acquired convolutional neural network structure, the network is trained with the expanded pathological image data set of esophageal cancer and the weights are fine-tuned to get the discriminant network model, and the intelligent discriminant is realized with the discriminant module. The invention overcomes the over-fitting problem in the depth learning process caused by the labeled esophageal cancer pathological imagedata set as a training sample due to the lack of large-scale disclosure, and improves the recognition rate.

Description

technical field [0001] The present invention relates to an automatic identification device and identification method for pathological slice images of esophageal cancer, in particular to an automatic identification device and identification method for esophageal cancer pathological images based on a convolutional neural network. Background technique [0002] According to statistics, the incidence of cancer has been increasing year by year in recent years. Taking Huai'an City, Jiangsu Province as an example, the mortality rate of malignant tumors is as high as 164.57 / 100,000 (the standardized death rate is 138.20 / 100,000), which is 39% higher than the national average. Among malignant tumors, esophageal cancer is the most lethal, accounting for 49.53% of cancer mortality. The reason for the high incidence of esophageal cancer is not only the external carcinogenic environmental factors and bad eating habits, but also the untimely detection of the disease. Most esophageal canc...

AI Technical Summary

Problems solved by technology

Since the features extracted manually rely on professional experience to a large extent, the objectivity is poor, it cannot represent the comprehensive information of the picture, and it is difficult to achieve the fusion of multiple artificial features, the classification effect is not ideal and it is not universal

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